{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_exp data.image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imaging Time Series\n",
    "\n",
    "> Main functions used to transform time series into TSImage tensors.\n",
    "\n",
    "> You can get more details on all TS to image transforms in the excellent pyts library:\n",
    "Johann Faouzi and Hicham Janati. pyts: A python package for time series classification.\n",
    "Journal of Machine Learning Research, 21(46):1−6, 2020.\n",
    "\n",
    "> https://pyts.readthedocs.io/en/stable/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from tsai.imports import *\n",
    "from tsai.utils import *\n",
    "from tsai.data.external import *\n",
    "from tsai.data.core import *\n",
    "from tsai.data.preprocessing import *\n",
    "from tsai.data.transforms import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from fastai.vision.augment import *\n",
    "from fastai.vision.core import *\n",
    "from matplotlib.backends.backend_agg import FigureCanvasAgg\n",
    "from pyts.image.gaf import GramianAngularField\n",
    "from pyts.image import MarkovTransitionField, RecurrencePlot\n",
    "from pyts.multivariate.image import JointRecurrencePlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsid = 'NATOPS'\n",
    "X, y, splits = get_UCR_data(dsid, return_split=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class TSImage(TensorImage):\n",
    "    def __getitem__(self, idx):\n",
    "        res = super().__getitem__(idx)\n",
    "        return res.as_subclass(type(self))\n",
    "\n",
    "    @property\n",
    "    def vars(self): \n",
    "        return self.shape[-3]\n",
    "\n",
    "    @property\n",
    "    def len(self): return self.shape[-2:]\n",
    "        \n",
    "    def __repr__(self):\n",
    "        if self.ndim == 0: return f'{self}'\n",
    "        else: return f'TSImage(shape:{self.shape})'\n",
    "\n",
    "    def show(self, **kwargs):\n",
    "        if self.ndim < 3: \n",
    "            while True:\n",
    "                self = self[None]\n",
    "                if self.ndim == 3: break\n",
    "        elif self.ndim > 3:\n",
    "            while True:\n",
    "                self = self[0]\n",
    "                if self.ndim == 3: break\n",
    "        if self[:3].shape[0] == 3 and kwargs == {}: \n",
    "            display(to_image(self[:3]))\n",
    "            return\n",
    "        else: \n",
    "            TensorImage(self[:3]).show(**kwargs)\n",
    "            return\n",
    "\n",
    "\n",
    "class ToTSImage(Transform):\n",
    "    order = 99\n",
    "    \"Transforms an object into TSImage\"\n",
    "    def encodes(self, o: TensorImage): return TSImage(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class TSToPlot(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by creating a matplotlib plot.\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size:Optional[int]=224, dpi:int=default_dpi(), lw=1, **kwargs):\n",
    "        self.size, self.dpi, self.lw, self.kwargs = size, dpi, lw, kwargs\n",
    "        self.fig = get_plot_fig(size, dpi=dpi)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        device = o.device\n",
    "        if o.data.device.type == 'cuda': o = o.cpu()\n",
    "        if o.ndim == 2: o = o[None]\n",
    "        seq_len = o.shape[-1]\n",
    "        fig = self.fig\n",
    "        if self.size is None: fig.set_size_inches(seq_len / self.dpi, seq_len / self.dpi)\n",
    "        canvas = FigureCanvasAgg(fig)\n",
    "        ax = fig.get_axes()[0]\n",
    "        ax.set_xlim(0, seq_len - 1)\n",
    "        output = []\n",
    "        for oi in o:\n",
    "            start = time.time()\n",
    "            ax.plot(oi.T, lw=self.lw, **self.kwargs) \n",
    "            canvas.draw()\n",
    "            buf = np.asarray(canvas.buffer_rgba())[..., :3]\n",
    "            output.append(tensor(buf / 255).permute(2,0,1)[None])\n",
    "            del ax.lines[:len(ax.lines)]\n",
    "        return TSImage(torch.cat(output)).to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 224, 224)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FD4B4D40350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToPlot()(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class TSToMat(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by creating a matplotlib matrix.\n",
    "    Input data must be normalized with a range(-1, 1)\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, dpi=default_dpi(), cmap=None, **kwargs):\n",
    "        self.size, self.dpi, self.cmap, self.kwargs = size, dpi, cmap, kwargs\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        device = o.device\n",
    "        if o.data.device.type == 'cuda': o = o.cpu()\n",
    "        if o.ndim == 2: o = o[None]\n",
    "        nvars, seq_len = o.shape[-2:]\n",
    "        aspect = seq_len / nvars\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        fig = get_plot_fig(self.size, dpi=self.dpi)\n",
    "        ax = fig.get_axes()[0]\n",
    "        ax.set_xlim(0, seq_len-1)\n",
    "        canvas = FigureCanvasAgg(fig)\n",
    "        output = []\n",
    "        for oi in o:\n",
    "            if output == []: im = ax.imshow(oi, aspect=aspect, vmin=-1, vmax=1, cmap=self.cmap, **self.kwargs) \n",
    "            else: im.set_data(oi)\n",
    "            canvas.draw()\n",
    "            buf = np.asarray(canvas.buffer_rgba())[..., :3]\n",
    "            canvas.flush_events()\n",
    "            output.append(tensor(buf / 255).permute(2,0,1)[None])\n",
    "        return TSImage(torch.cat(output)).to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 224, 224)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOAAAADgCAIAAACVT/22AAARLklEQVR4nO2d228k13HGT1/mTnJIrrjLXXJ3ad0taXXbyHKUCLKVKIYMBEYuBpw3A/4DggRO3oM8JQYMAUHyF/jFEGAb8IsdIUZkyYERK5BkWbK9G9F74/JOzpDDufUtDxp3fTXLpmkLlovB93uqnjrTfbr7636o6lPlZVnmnLv8pa+6O/Ay2ADbj9Hh/BhGBWoPgykvt/tzYvcWYhx2bmkrt5+au4GuL87+ILdbaS23X7r1Ag67unVXbieJ7wp4bul/c/vnrTPo2utXcnv3VhNdtZUwtxde7ea2//pbRQc6uYTnF3O7+9A8uqIJubtKG5pM7rPzUu0DVzAQX1zTtyxzzrn/evnLzrnCe0mIBShQYhoKlJiGAiWmoUCJaShQYhoKlJiGAiWm8T4I1L/gf/6jPGp4VkWAt59fyu3Np9TICw+v5vZWp5HbnbUJHBYcyJPmpR66MG48fQUO+pRKFrhQQs9n5lvo+eNzP8/tqh/ldpKpx/udvXO53R7W0LWxL7ONIY+QpmoPcSSR8DRRZ5HGcIJdSRxUNlVqpHlVot+nvndN7Xx1zZ0oXklfdnyDEuNQoMQ0FCgxDQVKTEOBEtNQoMQ0FCgxDQVKTEOBEtOMMkkvnv/rw5yQzMgk0ZINBjgq2dr+LU3OOeddfji3dy5N5bYfqTUHuAIhDXQmCVItYV/GHcyrh7OxKjmYqeUDdPnLK7mdbO8cc+ZFeKHkgYKFs+hKZuUEXaiml1RguUUqZ+HFalGFl8hmsKvOIquWxfYhm1UvFU3V70dqGw7lpbKR6amibMam5yLJ3g0XZF1NeW3/zmHfufLPjm9QYhwKlJiGAiWmoUCJaShQYhoKlJiGAiWmoUCJaUZB48bXB3f6fAh/1wKJ2S7VVGR+sSyxa1wR4ZxbjyQY+/rOPbn9zq0FHOZdlwUSjZsqzI5PUPeMzGfhVXWgoJ/I3orLBmG6YUIfKNzYy+39R+bQdetL9+b2/EU59+9c+hoOa/pqmcf/A17aXcLNd/albNNKtzk++pdMlERLaaYu8jCVJEXdkzxCNVB3E//FNygxDQVKTEOBEtNQoMQ0FCgxDQVKTEOBEtNQoMQ0o8Bp+w9/xVfxu2DfdmXtnHfHIH1WwrzVT6qYdvfRXm6ff2YVXWdr7dx+5b2HcrvUUaHdway0QMjGvqgveAaDgQroD6dP5fba0+o/n3r8J7l9oSZZiX/deRyHbUSTuX2tcwpd612pzVQJJKcwFsfe7cplGQxCdMW4uSffwFfXVG2m2Z/JzhvffANdLk3ch6VTYCv2ixzFHJIlcqMP+PkGJaahQIlpKFBiGgqUmIYCJaahQIlpKFBiGgqUmIYCJaYZ5SfCxYVDnEW1mQ66OCrZxTRTIf5rb+b2udcKh40lFd77s6dze+a0PE7h+hYOy/xp2fBUesYfQgYFKgoFG20c5kJJydy70kDPuz99JLdvXevndvm6Sr+lTflXb1E1IWk/Kbmf/n2yhwfPq84bfzQv7UTOlluugAjKTXWSqjpQLLmo737xQXTtrEItpE3JS1U31eVqrMklaqyouxG2JeHnRXBVvbHUHbz1xso2gYrQ9pLCZTp8gxLTUKDENBQoMQ0FSkxDgRLTUKDENBQoMQ0FSkwzaqLw3Iv/9Ov8R29ilFW7slAXWsp/99XvcU2ek+GE7lANa0Mqe1AralPX94fjepnurwAtB9JAdh7X1WKJ6oZEocMttWwhXr52xxmcALxKBTf9KenQ4NUlvI/5Bedc0pB/RVOqv4JaS4M3cCzKfkRtLK/Azg4Z9v1v/73jG5QYhwIlpqFAiWkoUGIaCpSYhgIlpqFAiWkoUGKa0ZfVtfe3nY5puzvC6Tnjw4JilcO31sl0PbeDjvpU29uVBgbdRxfRNWzKt9+VHQnOh/tDHJZAy96xaWOyIKnKVP1INeLtn5aUQPQxFbvuffZcbqcQuh7qPgJxHTICpbGMBZhlWJtQ0s2As8Mv+Djwp6CrLn65JZu1DTUHTHNUd6TrcHmnj8PCHeltEG6PpWRgeniRveJpZ4V7wA/vx3IrmccmCuSEQIES01CgxDQUKDENBUpMQ4ES01CgxDQUKDENBUpMM8rTXPvC2eP/x9PpDz8GW5dWwk4aPhTzqbTqOCwNZ3N75i1V8KgG+YbeorTRaD2gih9hKmJserhZOpCN2qpa1zGck+zRWGIjrmIuSn4PVYkq52EXDZ0TiqZkEzpSOz9Sy07Q5Y5IKuH6lrGTBddwSq+rqcvm3kXp05KGqmdLVpKVIb7K1o2tq4Gf9VQzdU7H20Pxe5JvUGIaCpSYhgIlpqFAiWkoUGIaCpSYhgIlpqFAiWlGoeE//8vitgZ3EOjocAki1P5RhXmETqLKBpUgiL8bqRj+fbUN2Tkcd5CqskEJxLXTI8K+gK/PohlIbabL1Wvourck02v6qpH4CaKTytKOZcitvDNQ/TPWI1nI8m7nHLp6CbSCSFS3cCT05cL6uo5XkTwmS/1Df3d8gxLjUKDENBQoMQ0FSkxDgRLTUKDENBQoMQ0FSkwzCre++bml33wfWKpprE4TVu2BTsNZQ4e7E3ENz06h50pbvtH3B9A4YVO1WPbKELf3j/fUjVWegql+q/IMerY+OSd/go4RSUntYTArm7HKNiiSagbDdE0iqOiU+cUNgPvQ9bmn5lBuy2ZjRWUiqruSbii35Fv5cFctDPD2YTPBRQKasDBQP8iKkzUoD7hN7VStjxjdi2Xn+AYlxqFAiWkoUGIaCpSYhgIlpqFAiWkoUGIaCpSYhgIlphnlA27/6flf40/F7Ze9I7II4Cl31DAs4VTqqPxHUoHmGxOSbwjumcZhXnyspSZHAY8qtgd3unJQ56xs9O/SU52QvItXi10BXiBTDUKVqgnA5ftq50WNNMaud5TI9LYeV0WSkgFs9mXJTamtsl7hgRyp3FI79+Ei+4Xnp6d3vLYlRxSi4huUmIYCJaahQIlpKFBiGgqUmIYCJaahQIlpKFBimlGgfvJW7O5oZO0lGIIHUwdpcRiuiHDOpdAKezgpgeK9JfVgRJPyr4nH1FqOj5+S2kyrXVkNsvyLM2oSWYGt8aoSGM9SdbJ+SxaNTFxX05u5IktNTn9vLbfj6zcLj3Ry8CqqTlZwbj63hwsz6ErLcgePV//K6dJMLgugHTde/uKG3nyDEtNQoMQ0FCgxDQVKTEOBEtNQoMQ0FCgxDQVKTONlWeace8H//Ed51PCi+oC/+6BE3dd+XzXfHdwt5fWDNQkp11dUaBdbKhzRbRfDy6k6jiqTlJxRbX4bTemvkEF8OdAfvYeBZAH6Q9Xjod+Tg6mYtA5kZwnEsXUeQW325AxLLXW2Dbgsp95TnQkqV9dzO7614szzSvqy4xuUGIcCJaahQIlpKFBiGgqUmIYCJaahQIlpKFBiGgqUmGaUSXr2c19xznlp4YINaLntKtsDHBZ0Je/i3d5CV9aVlhFeIDmPZG/vmPMLF6QjdDojSz68VGVxMuzsEerKSrjMALpMDOaqOKy6AXmXWO0cn+JgS2ae9dV1SNZldYqnm2AEFxZl2Ewjt9OyGhZNSf4pC3WqLMSzKFg44VzYTw/9y9i/cFXMWOINdxgMlR6wf/kRSz7wQGOKQhXhcTO95OODCl+vf+PvHN+gxDgUKDENBUpMQ4ES01CgxDQUKDENBUpMQ4ES04wCxZ/4hx8dPc6HxQnNoIeuyUBC3HOhisD3M4k83x5KoZ83Whdw2NVt6Xd9cEO148Zi/UldIsUTyyrEHR6IPR4cLijbNBagbt09mdvth1T1qT947EpuPzl1I7f/dnbZHZc3jj3yd8xWIpfya3sPo+ta/67cbkfQ3CJVl9KHgH7gFRbK8uFmTJb6xcMIMQwFSkxDgRLTUKDENBQoMQ0FSkxDgRLTUKDENB9dbSb/sY/n9u4jTXTt3S3PSe+iqouEX3hXVyTsP/dWrEfBR+ZjDx1+SA72WEvj4aT42g+oJsRZXTZnTu/ndhioD+9j6DQ8iFQeoduWsHZYLWwUnGJtpkSvC4DaTN6BBMbLOypIPnldot+n3lZJE+/d9+VA/cLAuB1Ym4mcAChQYhoKlJiGAiWmoUCJaShQYhoKlJiGAiWmoUCJaX6Z8PjEpUOc2J0b1lFghSPnnN+X1EjQPkBXvHxNdvD2T3O7+bY6DqaV/HodXb3nZNVBCPmP8npH7SJWuR9FQavnrBLqTUlTzVxVHar3zosr7MvCFV+nhHzYX3xGXaLgnOScYmhxPTHbxWELzbYcSLcQmShJHahhItmjfqLaifRi2Vz+E5Wui/bkFoe7MtdyS12f6o7c6PqGmkN5D250Hxqb6wuM7dmzsn4DYnUnLOGUHbEyhBDDUKDENBQoMQ0FSkxDgRLTUKDENBQoMQ0FSkwzCtiOdRT4AFxHgdFXP9HFj+oS9R3O1dDVf2o+t3un5GHoXFB7mLkkrRfqpQhdf7Hw77K3TA706tb9OKwzlNB6lOogOZYKUtFvdaBhIktNVt9XyYLmu3Id5t5o5TamHsZoFjmOBC9KpF27x9sDthj/2G80h2BO6mRFDyygK4EbHTfE9rQe1GnoGL5q5ICkDNSTkwkFSkxDgRLTUKDENBQoMQ0FSkxDgRLTUKDENB9dbSbEbzRw8+AF+Wx+9371oXvnfijVFMnjdOZ19Wgl5cLaTMVl/BXblyGeP6PaGN93VtoYz1bkG/idgYrn7/Rks32gEhbDvpxUEMqBMv0xOm5inSbnXBbLWXk9qM20pWozTS3L2c79xw10xbdW3ImCtZnICYACJaahQIlpKFBiGgqUmIYCJaahQIlpKFBiGgqUmGaU4Yifv+zuKJGDfa0zqNMU7qtGHP5NSbQk6xvuGKQHqoRT7Vv/LbYeGdx1Krf7jy/JQWNVGCmpSkIlLakcjBfLWQRDyeKMde1urMoeGj9TKyzS9VZub3axmlILhzULbOec82Xn4QVZR9G5NI+jMAeWVHSqDEowYW4s81T5JNxKpyfRNXz49+BAkLKq6JQVrPMpt9VFxiuGyzzSkpoqXn+84M65FJZ8ZKH8Kw31LQMd8g1KTEOBEtNQoMQ0FCgxDQVKTEOBEtNQoMQ0FCgxzShQ/+mXfuCcS/RqCaxqVPKkaH4zUKX/99MquHro2ookVny1ezq3r7TmcNjq+rRM6LZqYBBNQ3eEqtjTP9LFpCDQW2mp4HDUOLweUOlArwWBUckTKn6++YWp3H724nJu/9vi99UOPbX6opj/Od6w3w3tVO7gv+w8ga6taELsgdgV3U2iEhT2Gx8ksvSlFkj5qdBXPTBQbHyDEtNQoMQ0FCgxDQVKTEOBEtNQoMQ0FCgxDQVKTDMKnL726CFNFIppFLsmixzx89L2oPW0isa7B6WN8dwT6+i5fVO+qHdQkygYFobZZ9/cQc/6s7IHTET0TuuHE/a3p0+iUZNSTet98f3V8mdw2JPNm7n9w13VwmAbyjaVA4lCR4mK7bd7cheiSLmGXemPkHXFVV1XpawmbshpnP7P2+iKf3HdfVgwAt/60Hv7VaTO8Q1KjEOBEtNQoMQ0FCgxDQVKTEOBEtNQoMQ0FCgxDQVKTDPq8vHiPV92zjldmykrQcvlCLIIA1WbKV5d++3Nb+1vnsntyq5Mb/Yn+2oczLw/r5pvVNd7hw5LJrB3tSuttHJ7rGzT3mNncnvi/bYMi9RChe7SdG5HkyoP1FmAVuTnYSHNoqpRdbrZEbuuTnCYyr1Yamzn9g/Xl3DYfldyUf0tVeeqsiF7qK/JCU6sqrOobsrNDdp9dHkprKWJ5V9ZtYTDHIzKqirR5fVBRVCbyRuqVSKZ7zvnvvvjf3R8gxLjUKDENBQoMQ0FSkxDgRLTUKDENBQoMQ0FSkzzf3L65ii8a0n0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FD4B66FE510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToMat()(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 224, 224)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FD4B69D2190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToMat(cmap='spring')(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "@delegates(GramianAngularField.__init__)\n",
    "class TSToGADF(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Gramian Angular Difference Field.\n",
    "    It requires either input to be previously normalized between -1 and 1 or set range to (-1, 1)\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, range=None, **kwargs): \n",
    "        self.size,self.cmap,self.range = size,cmap,range\n",
    "        self.encoder = GramianAngularField(image_size=1., sample_range=self.range, method='d', **kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='linear', align_corners=False)[:, 0]\n",
    "        else: \n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size) / 2 + .5\n",
    "        if self.cmap and output.shape[1] == 1: \n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "    \n",
    "    \n",
    "@delegates(GramianAngularField.__init__)\n",
    "class TSToGASF(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Gramian Angular Summation Field.\n",
    "    It requires either input to be previously normalized between -1 and 1 or set range to (-1, 1)\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, range=None, **kwargs): \n",
    "        self.size,self.cmap,self.range = size,cmap,range\n",
    "        self.encoder = GramianAngularField(image_size=1., sample_range=self.range, method='s', **kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='linear', align_corners=False)[:, 0]\n",
    "        else: \n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size) / 2 + .5\n",
    "        if self.cmap and output.shape[1] == 1: \n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "    \n",
    "\n",
    "\n",
    "@delegates(MarkovTransitionField.__init__)\n",
    "class TSToMTF(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Markov Transition Field\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, n_bins=5, **kwargs): \n",
    "        self.size,self.cmap = size,cmap\n",
    "        self.encoder = MarkovTransitionField(n_bins=n_bins, **kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='linear', align_corners=False)[:, 0]\n",
    "        else: \n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size)\n",
    "        if self.cmap and output.shape[1] == 1: \n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "    \n",
    "    \n",
    "@delegates(RecurrencePlot.__init__)\n",
    "class TSToRP(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Recurrence Plot. \n",
    "    It requires input to be previously normalized between -1 and 1\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, **kwargs): \n",
    "        self.size,self.cmap = size,cmap\n",
    "        self.encoder = RecurrencePlot(**kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='linear', align_corners=False)[:, 0]\n",
    "        else: \n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()) / 2\n",
    "        output = output.reshape(bs, -1, size, size)\n",
    "        if self.cmap and output.shape[1] == 1: \n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "    \n",
    "    \n",
    "@delegates(JointRecurrencePlot.__init__)\n",
    "class TSToJRP(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Joint Recurrence Plot\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, **kwargs): \n",
    "        self.size,self.cmap = size,cmap\n",
    "        self.encoder = JointRecurrencePlot(**kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        o = to3d(o)\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len: o = F.interpolate(o, size=size, mode='linear', align_corners=False)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size)\n",
    "        if self.cmap and output.shape[1] == 1: \n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 24, 224, 224)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FD4B6AA7C90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToRP()(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o = TSTensor(X[0][1][None])\n",
    "encoder = RecurrencePlot()\n",
    "a = encoder.fit_transform(o.cpu().numpy())[0]\n",
    "o = TSTensor(X[0])\n",
    "encoder = RecurrencePlot()\n",
    "b = encoder.fit_transform(o.cpu().numpy())[1]\n",
    "test_eq(a,b) # channels can all be processed in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[0], TSToRP()(TSTensor(X[0][0][None]), split_idx=False)[0])\n",
    "test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[1], TSToRP()(TSTensor(X[0][1][None]), split_idx=False)[0])\n",
    "test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[2], TSToRP()(TSTensor(X[0][2][None]), split_idx=False)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Plot TSImage(shape:(8, 3, 100, 100)) torch.float32 0.10196078568696976 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FD49A177E90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Mat TSImage(shape:(8, 3, 100, 100)) torch.float32 0.007843137718737125 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FD49A13BC10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 GADF TSImage(shape:(8, 24, 100, 100)) torch.float32 0.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FD4988A4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 GASF TSImage(shape:(8, 24, 100, 100)) torch.float32 0.0 1.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FD4B4D40610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 MTF TSImage(shape:(8, 24, 100, 100)) torch.float32 0.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FD4985DEB50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 RP TSImage(shape:(8, 24, 100, 100)) torch.float32 0.0 0.6955705881118774\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FD499287750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsid = 'NATOPS'\n",
    "X, y, splits = get_UCR_data(dsid, return_split=False)\n",
    "tfms = [None, Categorize()]\n",
    "bts = [[TSNormalize(), TSToPlot(100)], \n",
    "       [TSNormalize(), TSToMat(100)], \n",
    "       [TSNormalize(), TSToGADF(100)], \n",
    "       [TSNormalize(), TSToGASF(100)], \n",
    "       [TSNormalize(), TSToMTF(100)], \n",
    "       [TSNormalize(), TSToRP(100)]]\n",
    "btns = ['Plot', 'Mat', 'GADF', 'GASF', 'MTF', 'RP']\n",
    "dsets = TSDatasets(X, y, tfms=tfms, splits=splits)\n",
    "for i, (bt, btn) in enumerate(zip(bts, btns)): \n",
    "    dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=8, batch_tfms=bt)\n",
    "    test_eq(dls.vars, 3 if i <2 else X.shape[1])\n",
    "    test_eq(dls.len, (100,100))\n",
    "    xb, yb = dls.train.one_batch()\n",
    "    print(i, btn, xb, xb.dtype, xb.min(), xb.max())\n",
    "    xb[0].show()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way to train a model using time series to image transforms is this: \n",
    "```\n",
    "dsid = 'NATOPS'\n",
    "X, y, splits = get_UCR_data(dsid, return_split=False)\n",
    "tfms = [None, Categorize()]\n",
    "batch_tfms = [TSNormalize(), TSToGADF(224)]\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "learn = tsimage_learner(dls, xresnet34)\n",
    "learn.fit_one_cycle(10)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.save_checkpoint();"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 000_utils.ipynb.\n",
      "Converted 000b_data.validation.ipynb.\n",
      "Converted 000c_data.preparation.ipynb.\n",
      "Converted 001_data.external.ipynb.\n",
      "Converted 002_data.core.ipynb.\n",
      "Converted 002b_data.unwindowed.ipynb.\n",
      "Converted 002c_data.metadatasets.ipynb.\n",
      "Converted 003_data.preprocessing.ipynb.\n",
      "Converted 003b_data.transforms.ipynb.\n",
      "Converted 003c_data.mixed_augmentation.ipynb.\n",
      "Converted 003d_data.image.ipynb.\n",
      "Converted 003e_data.features.ipynb.\n",
      "Converted 005_data.tabular.ipynb.\n",
      "Converted 006_data.mixed.ipynb.\n",
      "Converted 007_metrics.ipynb.\n",
      "Converted 008_learner.ipynb.\n",
      "Converted 008b_tslearner.ipynb.\n",
      "Converted 009_optimizer.ipynb.\n",
      "Converted 010_callback.core.ipynb.\n",
      "Converted 011_callback.noisy_student.ipynb.\n",
      "Converted 012_callback.gblend.ipynb.\n",
      "Converted 013_callback.MVP.ipynb.\n",
      "Converted 014_callback.PredictionDynamics.ipynb.\n",
      "Converted 100_models.layers.ipynb.\n",
      "Converted 100b_models.utils.ipynb.\n",
      "Converted 100c_models.explainability.ipynb.\n",
      "Converted 101_models.ResNet.ipynb.\n",
      "Converted 101b_models.ResNetPlus.ipynb.\n",
      "Converted 102_models.InceptionTime.ipynb.\n",
      "Converted 102b_models.InceptionTimePlus.ipynb.\n",
      "Converted 103_models.MLP.ipynb.\n",
      "Converted 103b_models.FCN.ipynb.\n",
      "Converted 103c_models.FCNPlus.ipynb.\n",
      "Converted 104_models.ResCNN.ipynb.\n",
      "Converted 105_models.RNN.ipynb.\n",
      "Converted 105_models.RNNPlus.ipynb.\n",
      "Converted 106_models.XceptionTime.ipynb.\n",
      "Converted 106b_models.XceptionTimePlus.ipynb.\n",
      "Converted 107_models.RNN_FCN.ipynb.\n",
      "Converted 107b_models.RNN_FCNPlus.ipynb.\n",
      "Converted 108_models.TransformerModel.ipynb.\n",
      "Converted 108b_models.TST.ipynb.\n",
      "Converted 108c_models.TSTPlus.ipynb.\n",
      "Converted 109_models.OmniScaleCNN.ipynb.\n",
      "Converted 110_models.mWDN.ipynb.\n",
      "Converted 111_models.ROCKET.ipynb.\n",
      "Converted 111b_models.MINIROCKET.ipynb.\n",
      "Converted 112_models.XResNet1d.ipynb.\n",
      "Converted 112b_models.XResNet1dPlus.ipynb.\n",
      "Converted 113_models.TCN.ipynb.\n",
      "Converted 114_models.XCM.ipynb.\n",
      "Converted 114b_models.XCMPlus.ipynb.\n",
      "Converted 120_models.TabModel.ipynb.\n",
      "Converted 121_models.TabTransformer.ipynb.\n",
      "Converted 122_models.TabFusionTransformer.ipynb.\n",
      "Converted 130_models.MultiInputNet.ipynb.\n",
      "Converted 140_models.misc.ipynb.\n",
      "Converted 900_tutorials.ipynb.\n",
      "Converted index.ipynb.\n",
      "\n",
      "\n",
      "Checking folder: /Users/nacho/Documents/Machine_Learning/Jupyter_Notebooks/tsai/tsai\n",
      "Correct conversion! 😃\n",
      "Total time elapsed 287 s\n",
      "Friday 02/04/21 15:18:30 CEST\n"
     ]
    },
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